Individuals and organizations use modeling to better understand processes that occur in the real world. For instance, an airline company may model the dynamics of forces on an airplane as it experiences various wind conditions. Alternatively, a car manufacturer may model the fuel consumption of an automobile under various loads. Further still, a marketing organization may want to model certain types of consumer behavior for one of its clients.
Computers are often used for modeling problems of any appreciable difficulty. With recent advances in computing systems, modeling of complex problems has become even more tractable. However, conventional modeling solutions are often targeted towards solving either a specific problem or handling a specific task in the end-to-end modeling process.
Because of these constraints, modelers have often had to cope with using multiple software packages altogether and/or suboptimal interfaces for communication between the various modeling modules, such as those for data gathering and/or model estimation. In these instances, modelers are frustrated by inefficiencies built into the modeling process; for instance, modelers may need to waste time formatting the output from one module so that data can be fed into a subsequent module to arrive at the modeled solution. In addition, modelers may be further hindered by insufficient automation and/or personalization available through the use of conventional modeling programs.
Therefore, there is a need for an end-to-end standardized modeling solution for creating models in any application domain.